The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent the work is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.
Gait recognition is becoming an increasingly popular research problem for human identification based on a walking style of the subject. Gait recognition has emerged as an attractive research problem as it possesses several desirable merits unlike other biometrics. However, most of the existing gait recognition methods (including those that involve Gabor-based filters) suffer from the curse of dimensionality. Some techniques employ a dimensionality reduction process, in order to make the gait recognition process feasible. However, such processes still incur a high computational and storage cost, and further incur difficulties in identifying subjects with a high degree of confidence.
Biometric recognition has been widely used as a powerful tool for automatic human identification and authentication. The gait biometric identifies subjects (i.e., people) by their way of walking. Unlike other biometrics like face and fingerprint, gait recognition does not request the target subject to interact in a predefined and cooperative manner such as being close to the acquisition device or standing at a specific angle. In gait-based systems, the process of image acquisition is non-intrusive. Thus, it can be done in public areas without attracting the attention of subjects under surveillance. Also the system can work at longer distances (e.g. 10 m or more), unlike most of the other biometrics. Moreover, the gait modality is difficult to disguise and can be of low resolution.
Gait recognition processes however have certain limitations. The processes can be greatly affected by a number of conditions like type of shoes, clothes, and the like that are worn by the subject in effectively recognizing the subject. Furthermore, the discriminating power of walking style can also be degraded by certain physical factors such as injuries. Nevertheless, as described by I. Bouchrika et al. in, “Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras,” Multimedia Tools and Applications, 2014, which is incorporated herein by reference in its entirety, gait is still a potential choice for intelligent visual surveillance and tracking of subjects.
Gait recognition processes can be classified broadly in two main categories: model based methods and model-free methods. In model based methods such as those described by D. K. Wagg et al. in, “On automated model-based extraction and analysis of gait,” proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, 2004, pp. 11-16; C. Yam et al. in, “Automated person recognition by walking and running via model-based approaches,” Pattern Recognition, vol. 37, no. 5, pp. 1057-1072, 2004; and by L. Wang et al. in, “Automatic gait recognition based on statistical shape analysis,” IEEE Transactions on Image Processing, vol. 12, no. 9, pp. 1120-1131, 2003, which are incorporated herein by reference in their entirety, the search for the human movement parameters is usually guided by a statistical or generic model. Here, the frequency and amplitude are typically merged with extracted features, or the collection of images is directly used.
In contrast, the model-free approaches such as those described by J. E. Boyd et al. in “Phase in model-free perception of gait,” in Human Motion, 2000, proceedings workshop on, 2000, pp. 3-10, and Y. Dupuis et al. in “Feature subset selection applied to model-free gait recognition,” Image and vision computing, vol. 31, no. 8, pp. 580-591, 2013, which are incorporated herein by reference in their entirety, use static and dynamic components. The static components reflect the shape and size of a human body, whereas the dynamic components reflect the movement dynamics. Examples of static features are height, width, stride length, and silhouette bounding box lengths, whereas frequency and phase of movement are examples of dynamic features. Furthermore, model-free techniques may also be classified into temporal and spatio-temporal methods. The research on model-free systems is relatively more than that on model-based systems, because of the computationally tractability.
Temporal gait recognition approaches are expensive in terms of storage and computation due to the frame by frame feature extraction and classification. Gait energy images (GEIs) represent the human walking in a single image conserving motion temporal properties. Several gait recognition approaches rely on features extracted from GEIs. However, such gait recognition processes use reduced-dimensionality GEIs, or apply the feature extraction algorithm on the holistic GEI.
Additionally, Gabor filters have been widely used as an effective feature extraction approach in many fields of research. Such filters have been also utilized in many biometric applications such as iris recognition, and face recognition. However, a main problem incurred with the use of the Gabor filter is the huge dimensionality caused by a convolution process. Few attempts have been proposed for using Gabor filters in gait recognition. For example, the work of Huang et al. “Gait recognition based on Gabor wavelets and modified gait energy image for human identification,” Journal of Electronic Imaging, vol. 22, no. 4, October 2013, which is incorporated herein by reference in its entirety, applied a Gabor filter on a modified version of GEI representation. A three-step search (TSS) algorithm was used to prevent the confliction of treating multi-walkers in a silhouette as one person. Adaptive background model was also utilized to alleviate the effect of illumination variance and slow walking. The dimensionality was reduced using PCA and an SVM with RBF-kernel was trained and used for classification.
Accordingly, the present disclosure provides for a framework of gait recognition and characterization, which is not computationally and/or storage wise intensive. The gait recognition technique of the present disclosure provides an efficient and cost-effective manner of recognizing objects.